Public trust in governments, health care providers, and the media during pandemics: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among the most important factors that determine whether public health recommendations receive widespread adherence during pandemics is public trust in the information disseminated by governments, health care providers, and the media. However, there remains uncertainty pertaining to the role of public trust in the acceptance and maintenance of public health recommendations during outbreaks. This systematic review and thematic analysis examined 41 studies on previous pandemics, epidemics, and global outbreaks in the twenty-first century to identify the relationship between public trust in the government, health care providers, and the media, and the acceptance, uptake, and maintenance of health behaviours that contain the spread of infectious disease. We found inconsistency in public trust towards the government and the media across multiple countries, while trust in health care providers was generally reported to be high with a few exceptions. We identified several unintended outcomes of mistrust when communicating public health recommendations such as non-compliance with recommended health measures, seeking information from alternative sources, and vaccine hesitancy. We conclude this paper by discussing the importance of public trust in promoting compliance with public health recommendations and the uptake of protective behaviours, as well as the downstream implications of mistrust that may develop in the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it